Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "128" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 27 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 27 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460010 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.160070 | -0.202268 | -1.403582 | -0.110935 | -0.129759 | -0.340701 | 0.272739 | 5.650725 | 0.6196 | 0.6332 | 0.3626 | nan | nan |
| 2460009 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.158490 | -0.825563 | -1.322901 | -0.119943 | -0.433260 | -0.512749 | 0.947547 | 2.896300 | 0.6199 | 0.6317 | 0.3664 | nan | nan |
| 2460008 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.562284 | -0.346508 | -1.343270 | 0.318897 | -0.886190 | -0.780567 | -1.030205 | 1.105511 | 0.6631 | 0.6715 | 0.3289 | nan | nan |
| 2460007 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.247439 | -0.508475 | -1.237070 | -0.527120 | 0.144871 | -0.403475 | 0.130927 | 5.052435 | 0.6277 | 0.6414 | 0.3541 | nan | nan |
| 2459999 | digital_ok | 0.00% | 99.08% | 99.50% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.2676 | 0.2089 | 0.2193 | nan | nan |
| 2459998 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.140183 | -0.489851 | -1.092395 | -0.329050 | -0.077358 | 0.985864 | 1.505478 | 6.267962 | 0.6312 | 0.6456 | 0.3848 | nan | nan |
| 2459997 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.068695 | -0.603279 | -1.190092 | -0.174148 | -0.287188 | -0.111336 | 2.068006 | 6.458143 | 0.6460 | 0.6607 | 0.3873 | nan | nan |
| 2459996 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.205746 | -0.640656 | -0.924318 | -0.022553 | -0.209779 | -1.084554 | 1.126127 | 1.927638 | 0.6449 | 0.6533 | 0.3992 | nan | nan |
| 2459995 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.141912 | -0.650664 | -1.508576 | -0.227785 | -0.572370 | 0.794245 | 0.638496 | 4.581867 | 0.6466 | 0.6579 | 0.3835 | nan | nan |
| 2459994 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.082881 | -0.471526 | -1.347395 | -0.412961 | -0.129981 | -0.091973 | 0.418287 | 3.865053 | 0.6417 | 0.6551 | 0.3812 | nan | nan |
| 2459993 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.569711 | -0.155934 | -1.258556 | -0.268441 | -0.799495 | 1.012056 | 0.191668 | 3.515572 | 0.6352 | 0.6607 | 0.3924 | nan | nan |
| 2459991 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.342101 | -0.302112 | -1.304807 | -0.281244 | -0.457590 | 0.561227 | 0.081532 | 4.626813 | 0.6418 | 0.6490 | 0.3902 | nan | nan |
| 2459990 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.587037 | -0.558342 | -0.672219 | 0.258934 | -0.536705 | 0.000810 | -0.551344 | 3.483973 | 0.6406 | 0.6490 | 0.3909 | nan | nan |
| 2459989 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.079477 | -0.374507 | -0.885199 | -0.275863 | -0.355546 | 0.530683 | -0.371211 | 2.674348 | 0.6389 | 0.6500 | 0.3911 | nan | nan |
| 2459988 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.241181 | -0.505125 | -1.235719 | -0.210903 | -0.508739 | 0.433686 | -0.131670 | 3.119775 | 0.6419 | 0.6520 | 0.3824 | nan | nan |
| 2459987 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.126189 | -0.410380 | -1.273151 | -0.374508 | -0.710724 | 0.092000 | -0.272647 | 6.034825 | 0.6430 | 0.6525 | 0.3840 | nan | nan |
| 2459986 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.114113 | -0.591508 | -1.381463 | -0.244289 | -0.912190 | 0.054393 | -0.763446 | 1.444403 | 0.6666 | 0.6797 | 0.3339 | nan | nan |
| 2459985 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.459945 | -0.627539 | -1.335758 | -0.320942 | -0.642841 | -0.130862 | 0.137441 | 6.251930 | 0.6460 | 0.6562 | 0.3841 | nan | nan |
| 2459984 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.600568 | -0.388858 | -1.181775 | -0.089611 | -0.690780 | -1.833241 | -0.702336 | 1.371700 | 0.6590 | 0.6725 | 0.3625 | nan | nan |
| 2459983 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 9.756171 | 12.969014 | 9.972711 | 10.441806 | 9.177569 | 11.042746 | 2.744396 | 6.156116 | 0.0317 | 0.0431 | 0.0061 | nan | nan |
| 2459982 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.140515 | 10.666906 | 0.942707 | 8.902301 | 0.771510 | 5.207802 | 0.339947 | 3.292894 | 0.7151 | 0.0408 | 0.4787 | nan | nan |
| 2459981 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.314955 | 11.973932 | 0.953605 | 11.116707 | 7.634815 | 12.238691 | -0.520113 | 0.984532 | 0.6369 | 0.0402 | 0.4707 | nan | nan |
| 2459980 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.767251 | 11.549014 | 6.840742 | 10.155865 | 2.754874 | 10.661969 | 2.624562 | 5.119392 | 0.5948 | 0.0348 | 0.3994 | nan | nan |
| 2459979 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.530340 | 11.637786 | 5.854013 | 9.005740 | 0.875382 | 9.979532 | -0.919246 | -0.215722 | 0.5329 | 0.0333 | 0.3825 | nan | nan |
| 2459978 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.278652 | 12.284146 | 6.916616 | 10.234346 | 1.086699 | 10.848195 | -1.037240 | 0.050776 | 0.5260 | 0.0306 | 0.3777 | nan | nan |
| 2459977 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.173783 | 12.992538 | 6.697720 | 10.077068 | 1.437182 | 11.160318 | -1.054190 | 0.275556 | 0.5006 | 0.0355 | 0.3549 | nan | nan |
| 2459976 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.627352 | 12.053469 | 6.612161 | 9.954608 | 1.668507 | 10.669946 | -0.848197 | 0.255366 | 0.5469 | 0.0330 | 0.3931 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 5.650725 | 0.160070 | -0.202268 | -1.403582 | -0.110935 | -0.129759 | -0.340701 | 0.272739 | 5.650725 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 2.896300 | 0.158490 | -0.825563 | -1.322901 | -0.119943 | -0.433260 | -0.512749 | 0.947547 | 2.896300 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 1.105511 | -0.346508 | -0.562284 | 0.318897 | -1.343270 | -0.780567 | -0.886190 | 1.105511 | -1.030205 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 5.052435 | 0.247439 | -0.508475 | -1.237070 | -0.527120 | 0.144871 | -0.403475 | 0.130927 | 5.052435 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 6.267962 | 0.140183 | -0.489851 | -1.092395 | -0.329050 | -0.077358 | 0.985864 | 1.505478 | 6.267962 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 6.458143 | -0.068695 | -0.603279 | -1.190092 | -0.174148 | -0.287188 | -0.111336 | 2.068006 | 6.458143 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 1.927638 | 0.205746 | -0.640656 | -0.924318 | -0.022553 | -0.209779 | -1.084554 | 1.126127 | 1.927638 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 4.581867 | -0.141912 | -0.650664 | -1.508576 | -0.227785 | -0.572370 | 0.794245 | 0.638496 | 4.581867 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 3.865053 | -0.082881 | -0.471526 | -1.347395 | -0.412961 | -0.129981 | -0.091973 | 0.418287 | 3.865053 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 3.515572 | -0.569711 | -0.155934 | -1.258556 | -0.268441 | -0.799495 | 1.012056 | 0.191668 | 3.515572 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 4.626813 | -0.342101 | -0.302112 | -1.304807 | -0.281244 | -0.457590 | 0.561227 | 0.081532 | 4.626813 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 3.483973 | -0.558342 | -0.587037 | 0.258934 | -0.672219 | 0.000810 | -0.536705 | 3.483973 | -0.551344 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 2.674348 | -0.374507 | -0.079477 | -0.275863 | -0.885199 | 0.530683 | -0.355546 | 2.674348 | -0.371211 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 3.119775 | -0.505125 | -0.241181 | -0.210903 | -1.235719 | 0.433686 | -0.508739 | 3.119775 | -0.131670 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 6.034825 | -0.126189 | -0.410380 | -1.273151 | -0.374508 | -0.710724 | 0.092000 | -0.272647 | 6.034825 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 1.444403 | -0.591508 | -0.114113 | -0.244289 | -1.381463 | 0.054393 | -0.912190 | 1.444403 | -0.763446 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 6.251930 | -0.627539 | -0.459945 | -0.320942 | -1.335758 | -0.130862 | -0.642841 | 6.251930 | 0.137441 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Discontinuties | 1.371700 | -0.600568 | -0.388858 | -1.181775 | -0.089611 | -0.690780 | -1.833241 | -0.702336 | 1.371700 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Shape | 12.969014 | 9.756171 | 12.969014 | 9.972711 | 10.441806 | 9.177569 | 11.042746 | 2.744396 | 6.156116 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Shape | 10.666906 | -0.140515 | 10.666906 | 0.942707 | 8.902301 | 0.771510 | 5.207802 | 0.339947 | 3.292894 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Temporal Variability | 12.238691 | 11.973932 | 0.314955 | 11.116707 | 0.953605 | 12.238691 | 7.634815 | 0.984532 | -0.520113 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Shape | 11.549014 | 11.549014 | 0.767251 | 10.155865 | 6.840742 | 10.661969 | 2.754874 | 5.119392 | 2.624562 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Shape | 11.637786 | 1.530340 | 11.637786 | 5.854013 | 9.005740 | 0.875382 | 9.979532 | -0.919246 | -0.215722 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Shape | 12.284146 | 12.284146 | 1.278652 | 10.234346 | 6.916616 | 10.848195 | 1.086699 | 0.050776 | -1.037240 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Shape | 12.992538 | 1.173783 | 12.992538 | 6.697720 | 10.077068 | 1.437182 | 11.160318 | -1.054190 | 0.275556 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 128 | N10 | digital_ok | nn Shape | 12.053469 | 12.053469 | 1.627352 | 9.954608 | 6.612161 | 10.669946 | 1.668507 | 0.255366 | -0.848197 |